EDP Project Report OSP Update

EMWG Forum Lloyd’s Old Library

10th December 2014

AGENDA

EDP • Introduction (Rob Stevenson) • Motivation • What was done • What was learnt • What has been produced • So What? • Implications • What Next?

Questions

OSP • Background (Trevor Maynard) • Oasis Vision and Lloyd's • OSP Timeline • Uncertainties • Model Validation • So What? • How to get involved Questions

Exposure Data Design Standards Project

EDP - AGENDA • Introduction (Rob Stevenson) • Motivation • What was done • What was learnt • What has been produced • So What? • Implications • What Next?

Questions

EDP Plan

Orientation Overview Requirements Design Evaluation Finalise Deliverables Communicate

Jun Aug Oct Task Jul Sep Nov

Orientation Meeting (19th June)

LMA EMWG Forum (10th July)

Requirements Meetings

Evaluation Workshop (9th Sept)

LMA

= Technical Steering Meetings = Other Meetings = EMWG Meetings

Outputs (30th June)

Plus monthly Progress Reports

EMWG Forum

(10th Dec)

Who’s Who Sponsor: LMA Board & David Gittings Overall Steering: EMWG, chair Rob Stevenson Technical Steering: TSG, chair Sian Fleming LMA: Gary Budinger Pat Hakong Margaret Fawke Project Team: Dickie Whitaker (Oasis Palm Tree) Peter Taylor (Oasis Palm Tree) Eliza Tadley (KCC) Ian Nicol (ATD) Phil Burgin (ATD) Richard Toothill (ATD)

Stakeholder Requirements Meetings

Who When No. of Attendees

No. of requirements

Software Houses 9th July 12 29 Brokers 15th July 7 12 Modelling Companies 16th July 6 10 Xchanging (XIS) 21st July 5 7 Carriers 22nd July 42 55 Transport providers (TMEL) 25th July 2 4

Summary of Requirements 1. Maintainability (change process)

2. Extensibility (applicability to new LOBs,

insurance types)

3. Data sharing (through a centralised model?)

4. Information management (exposure data, results, guidelines)

5. Scalability & performance (data access, storage, transport)

6. Interoperability (of new & existing formats)

7. Template amendments (spreadsheets)

• Commonality (over all data types, lines of business etc) • Flexibility (to handle new data formats) • Be controllable • Be quick & easy (not slow & bureaucratic!)

Lines of business: • Property • Offshore energy • Specialty Diverse data inputs into loss modelling formats

Types of insurance: • D & F • Treaty • Binders

Workstreams Ref Workstream Description Interest expressed by (Lead in bold) A1 Non-property Exposure Data Capture &

Database Talbot; Faraday; Navigators; Munich Re; Beazley; SCOR; Catlin; Atrium; AIR

A2 Binders Cleansing Kiln; Faraday; Navigators; Hardy; SCOR; Talbot; Catlin; OIM; ImageCat; Catex; LMA

A3 Treaty Exposure Database Talbot; Faraday; Hardy; SCOR; Catlin; Aspen Re; ImageCat

A4 Portfolio Data Lake Barbican; Mitsui; SCOR; Markel; ImageCat; IBM

A5 Results Data Lake SCOR; Navigators; Talbot; Markel; Aspen Re; IBM

B1 Business Implementation Guidelines (incorporating Data Quality & Project Information)

LMA; Aspen Re; Beazley; Faraday; Hardy; Navigators; SCOR; Talbot; Torus; Agencyport (Xuber); AIR; Catex; EQECAT; IBM; ImageCat; JBA

B2 Technology Oasis Palm Tree; Faraday; Navigators; SCOR; Talbot; Markel; AIR; Catex; EQECAT; IBM; JBA

C1 Hadoop ‘Show and Tell’ Oasis Palm Tree; IBM (BigInsights); NTT (Pivotal HD); Northdoor/Microsoft (HD Insight) …

C2 GIS ‘Show and Tell’ Oasis Palm Tree; Esri; ImageCat; RMA; Spatial Key …

EDP - What have we learnt? • Can automate formatted data

– Once formatted, data can be made interchangeable – Staging areas can be used to clean the data

• Can improve data quality and retain audit trail back to source

• Can reform data management + reduce unit costs – No longer constrained by rigidities and slowness of

traditional databases – Can use “Data Lakes” to review data and then transform

into databases for different user purposes – These are enterprise solutions not just for exposure and

cat modelling results and can be achieved in-house or using software houses

Improve Efficiencies

Formatted File Format

Clean

Possible Data Lake

Transform Load

Transformed and Cleansed

Canonical Data

Model

Review Source

Formatted File

Share

Analyse Source

Format Review Analyse Clean Load Share

Automate from format onwards

Validate

Validate

EDP - Management of Exposure Data

Calculation Engine

Input Exposures

and Policies

Outputs

User Interface

Input Models

Portfolio Results

Models Exposure Database Results Database

Workflow Reporting

Calculator

EDP - Filestream

SQLServer

Spreadsheet

e.g. Power Tools

Filestream Folder

view as source view transformed

Front-end Tools

EDP - Data Lakes

Idea of diagram thanks due to LOOM

Data Lake

Meta-data

Model

Mapforce

View Transform

XML, XSLT, SQL, MapReduce

EDP - “Big Data”

Federation Layer

Front-end Tools

MPP SQL Server Hadoop

e.g. Power tools

MS Power Tools, eg Power Map

EDP - Products

Business Implementation

Guidelines

Data and transformation

designs

Reference Computer Systems

Pilot Projects

LMA, LMA Website

ACORD, with link to LMA

LMA Market Processes (6 mths)

LMA Board sponsored

Non-property exposures Portfolio data lake Results data lake

Example BIGs

Information • Lloyd’s and LMA • Data Standards • Suppliers • Common Pitfalls • FAQs • Reference Implementations

Good Practice • Data Recording • Data Processing • Data Management • Catastrophe Models

US Property Binders

EDP - So what? • Efficiencies from Automation

– Redefine processes – Remove manual intervention – Re-use standard schemas and transformations

• Data Quality – Easier to attend to data if logistics sorted and tools provided – Can retain audit trail of versions and changes

• Data Management – Reduce reliance on any one external company – Scalable (can be done in volume), cheaper – NOT QUITE READY but … a fundamental change in enterprise

data management is underway • Options to do these in-house or use software houses • Reduce reliance on RMS

EDP - Implications • Automate data transformations to load exposure

and policy data • Provide full audit trail of data as they are changed

along the process • Manage very large datasets efficiently and flexibly • Create your own data stores for exposures and

cat modelling results • Use in-memory analytic tools now (many of these

features available within existing Microsoft products)

What Next? • EDP has shown there is potential for

– Streamlining data capture – Re-using data once captured automatically – Managing data with “Data Lakes”

• Market pilots to prove practical benefits A. Non-property exposures B. Property Exposures and Policies Data Lake C. Cat Modelling Results Data Lake

• Each to run for 6 months in 2014 if sponsoring Managing Agents volunteer

• Suggest (LMA produce) Invitation To Tender and get suppliers to quote

Oasis Solutions Project

OSP - AGENDA • Background (Trevor Maynard) • Oasis Vision and Lloyd's • OSP Timeline • Uncertainties • Model Validation • So What? • How to get involved Questions

Goals of Oasis • Improve risk assessment through better

models, transparency, performance, and innovation

• Provide open source software and an open framework for model and software development

• Establish a commercially vibrant community of providers and users of Oasis software, models, data, and tools

Core functions of Oasis • Build and maintain robust software (official

model versions)…and front end tba? • Build develop and sustain the community • Develop and maintain web portal, working

parties and e-commerce platform • Stimulate education & innovation in

catastrophe loss modelling and associated data

Oasis LMF Members: 22 -> 40 • ClimateKIC • Lloyd’s • SCOR • Catlin • Validus • Ren Re • Hiscox • TigerRisk Partners • Cathedral • Novae • Zurich • Liberty • Aspen

• Aon Benfield • Guy Carpenter • Willis • Partner Re • Allianz • Axis • Amlin • Tokio Millennium Re/Kiln • Suncorp • JLTRe • GenRe • Swiss Re • Beazley

• Argo • Ark • Ascot/AIG • Barbican • Brit • Canopius • Chaucer • Hardy • Mitsui Sumitomo • QBE • R&Q • C V Starr • W R Berkley • XL

and many more in the pipeline …

2015 Strategically • Preparation of impact for OSP • Next phase of big model integration:

– US Quake – Euro wind & storm surge – Japan

• Service community • Education & outreach

Back

End

: Se

rver

s

Database Servers N

etez

za

Web Server

Mid

dle

Tier

: Dj

ango

WS

over

VM

in

a V

M

Data Storage

R FE

Javascript Front-End in browser

Fron

t End

: Re

fere

nce

web

inte

rfac

es

Django RESTFul WebServices

File System

MyS

QL

SQLS

erve

r

Python/SQL mid-tier

ODBC

Django Admin

Excel webservice FE

ODBC ODBC

MySQL - Django and ‘internal MySQL’

Grid & Cluster Other Technologies

External Web Services

3rd Party Apps – eg Windows C#, Android,

etc

Loading tools/ webservices

Linkages to Third Party Products

Dist

ribut

ed

SQLS

erve

r

Hado

op

Bare

-m

etal

(C)

html5

C++/C

Mod

el

xsd/xslt

Oasis Technical Architecture

OSP - Timeline

OSP Components

OSP - Key Products Service OSP only

Available to market later

ValidationOasis Checklist (completed)MethodologyTools (e.g. reference vulnerability curves)Expert opinion on the sciencePalm Tree supportEvaluationTest planBenchmark exposure datasets for validationRun time environmentPalm Tree supportAdvice on hardware needsFront ends Extensible business front endLicensingShared service

OSP – Model Validation • The Science • Justification • Historical Event Results • Comparisons

– Event sets and footprints – Vulnerability Functions – Benchmark portfolios

• Fitness for Purpose • User Evaluation

Uncertainties in a Cat Loss Model

Damage Event

Ground-up Loss

Insured Loss

Loss to insurer

Interest

Policy

Primary – Event

Frequency

Clustering

Secondary - Event Footprint

Choice of peril intensity

Peril intensity

Secondary - Vulnerability

Vulnerability

Damageability

Exposure Data

Data quality

Location correlation

Stochastic Modelling

Discretisation error

Sampling error Socio-economic

“Loss amplification”

Legal judgements

ARA HurLoss focuses on

Occupancy - Industrial L.A., 1991, 2-story, Level 0 for all subtypes; 50 simulations,with 50,000-yr walkthrough each

TU: 40%BSW_C: 20%BSW_M: 20%W_E: 10%CBF: 7%LS: 3%

1000 Yr RP

Loss Distribution

Ensemble – Poor Data

Source: ImageCat, private communication

Better Data

CODA TypeL.A., 1991, 2-story, Level 2

TU: 100%

1000 Yr RP

Loss Distribution

Source: ImageCat, private communication

ARA Discrete Calculation

Source: ARA Hurloss Brochure 2012

ImageCat Robust Simulation …

Source: ImageCat RAA 2014 Presentation

Example Oasis Output

15% share of $1m xs $1m location deductible of $500

Ground-up Loss AEP

Layer AEP

Event Loss Distribution

OSP - So What? • The deep view of risk shows the full range of

potential outcomes • This in turn affects decisions and numbers for:

– Risk Selection – Pricing – Exposure Management – Capital

• And raises the bar for many existing models

OSP – How to get involved • Q1 2015

– Front-end Design – Model Validation – Benchmark Data Creation

• Q2 2015 – (Core User) Testing

• Q3 2015 – Market Evaluation

OSP Discussion